docstr: ivy.Array instance method variant of `ivy.max_pool1d`. This method
  simply wraps the function, and so the docstring for `ivy.max_pool1d`
  also applies to this method with minimal changes.
  
  Parameters
  ----------
  self
      Input image *[batch_size,w,d_in]*.
  kernel
      The size of the window for each dimension of the input tensor.
  strides
      The stride of the sliding window for each dimension of input.
  padding
      "SAME" or "VALID" indicating the algorithm, or list indicating
      the per-dimension paddings.
  data_format
      "NWC" or "NCW". Defaults to "NWC".
  dilaton
      The stride between elements within a sliding window, must be > 0.
  ceil_mode
      If True, ceil is used instead of floor to compute the output shape.
      This ensures that every element is covered by a sliding window.
  out
      optional output array, for writing the result to. It must have a shape that
      the inputs broadcast to.
  
  Returns
  -------
  ret
      The result of the max pooling operation.
  
  Examples
  --------
  >>> x = ivy.arange(0, 24.).reshape((2, 3, 4))
  >>> print(x.max_pool1d(2, 2, 'SAME'))
  ivy.array([[[ 4.,  5.,  6.,  7.],
          [ 8.,  9., 10., 11.]],
         [[16., 17., 18., 19.],
          [20., 21., 22., 23.]]])
  >>> x = ivy.arange(0, 24.).reshape((2, 3, 4))
  >>> print(x.max_pool1d(2, 2, 'VALID'))
  ivy.array([[[ 4.,  5.,  6.,  7.]],
     [[16., 17., 18., 19.]]])
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code: def max_pool1d(
      self: ivy.Array,
      kernel: Union[int, Tuple[int, ...]],
      strides: Union[int, Tuple[int, ...]],
      padding: Union[str, int, Tuple[int], List[Tuple[int, int]]],
      /,
      *,
      data_format: str = "NWC",
      dilation: Union[int, Tuple[int]] = 1,
      ceil_mode: bool = False,
      out: Optional[ivy.Array] = None,
  ) -> ivy.Array:
      """ivy.Array instance method variant of `ivy.max_pool1d`. This method
      simply wraps the function, and so the docstring for `ivy.max_pool1d`
      also applies to this method with minimal changes.
  
      Parameters
      ----------
      self
          Input image *[batch_size,w,d_in]*.
      kernel
          The size of the window for each dimension of the input tensor.
      strides
          The stride of the sliding window for each dimension of input.
      padding
          "SAME" or "VALID" indicating the algorithm, or list indicating
          the per-dimension paddings.
      data_format
          "NWC" or "NCW". Defaults to "NWC".
      dilaton
          The stride between elements within a sliding window, must be > 0.
      ceil_mode
          If True, ceil is used instead of floor to compute the output shape.
          This ensures that every element is covered by a sliding window.
      out
          optional output array, for writing the result to. It must have a shape that
          the inputs broadcast to.
  
      Returns
      -------
      ret
          The result of the max pooling operation.
  
      Examples
      --------
      >>> x = ivy.arange(0, 24.).reshape((2, 3, 4))
      >>> print(x.max_pool1d(2, 2, 'SAME'))
      ivy.array([[[ 4.,  5.,  6.,  7.],
              [ 8.,  9., 10., 11.]],
             [[16., 17., 18., 19.],
              [20., 21., 22., 23.]]])
      >>> x = ivy.arange(0, 24.).reshape((2, 3, 4))
      >>> print(x.max_pool1d(2, 2, 'VALID'))
      ivy.array([[[ 4.,  5.,  6.,  7.]],
         [[16., 17., 18., 19.]]])
      """
      return ivy.max_pool1d(
          self,
          kernel,
          strides,
          padding,
          data_format=data_format,
          dilation=dilation,
          ceil_mode=ceil_mode,
          out=out,
      )
  
imports: import abc
  from typing import Optional, Union, Tuple, List, Literal, Sequence, Callable
  import ivy
label_desc: Use image node_id 1 for calling the _ArrayWithLayersExperimental obj's underlying member method code with example usage: obj.max_pool1d() and returns: ivy